code: def test():
      url_zip_train = "https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/rcv1_train.binary.bz2"
      urllib.request.urlretrieve(url_zip_train, filename="train.bz2")
  
      f_svm = open("train.svm", "wt")
      with bz2.open("train.bz2", "rb") as f_zip:
          data = f_zip.read()
          f_svm.write(data.decode("utf-8"))
      f_svm.close()
  
      X, y = load_svmlight_file("train.svm")
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, test_size=0.33, random_state=42
      )
  
      pipeline = make_pipeline(
          FeatureGradientSelector(n_epochs=1, n_features=10),
          LogisticRegression(),
      )
      # pipeline = make_pipeline(SelectFromModel(ExtraTreesClassifier(n_estimators=50)), LogisticRegression())
  
      pipeline.fit(X_train, y_train)
  
      print("Pipeline Score: ", pipeline.score(X_train, y_train))
  
file_path: nni/examples/feature_engineering/gradient_feature_selector/sklearn_test.py
imports: import bz2
  import urllib.request
  import numpy
  from sklearn.datasets import load_svmlight_file
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import make_pipeline
  from sklearn.linear_model import LogisticRegression
  from sklearn.ensemble import ExtraTreesClassifier
  from sklearn.feature_selection import SelectFromModel
  from nni.algorithms.feature_engineering.gradient_selector import FeatureGradientSelector
label_desc: Use image node_id 1 for calling a global function with example usage: test() without return types